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Description: Swarm intelligence is an innovative computational way to solving hard problems.
This discipline is inspired by the behavior of social insects such as fish
schools and bird flocks and colonies of ants, termites, bees and wasps. In general,
this is done by mimicking the behavior of the biological creatures within
their swarms and colonies.
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Author: 黄先生 |
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Description: PSO’s precursor was a simulator of social behavior, that was used to visualize
the movement of a birds’ flock. Several versions of the simulation model
were developed, incorporating concepts such as nearest-neighbor velocity
matching and acceleration by distance
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Author: sina mehrabi |
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Description: Particle swarm optimization (PSO) was originally designed and introduced by Eberhart and
Kennedy (Ebarhart, Kennedy, 1995 Kennedy, Eberhart, 1995 Ebarhart, Kennedy, 2001). The
PSO is a population based search algorithm based on the simulation of the social behavior of
birds, bees or a school of fishes. This algorithm originally intends to graphically simulate the
graceful and unpredictable choreography of a bird folk. Each individual within the swarm is
represented by a vector in multidimensional search space.-Particle swarm optimization (PSO) was originally designed and introduced by Eberhart and
Kennedy (Ebarhart, Kennedy, 1995 Kennedy, Eberhart, 1995 Ebarhart, Kennedy, 2001). The
PSO is a population based search algorithm based on the simulation of the social behavior of
birds, bees or a school of fishes. This algorithm originally intends to graphically simulate the
graceful and unpredictable choreography of a bird folk. Each individual within the swarm is
represented by a vector in multidimensional search space.
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Author: Beta |
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Description: Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling. Each particle keeps track of its coordinates in the problem space which are associated with the best solution (fitness) it has achieved so far. (The fitness value is also stored.)
This value is called pbest. Another "best" value that is tracked by the particle swarm optimizer is the best value, obtained so far by any particle in the neighbors of the particle. This location is called lbest. when a particle takes all the population as its topological neighbors, the best value is a global best and is called gbest. Following is the steps of PSO:
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Author: BBB |
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Description: 主要是关于一些粒子演算法的思想及如何进行分析。并且粒子演算法的一些衍生算法- PSO is a recently proposed algorithm, motivated
from the simulation of social behavior.
PSO is based on the evolutionary computation
technique.
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Size: 6055936 |
Author: dfa |
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Description: iccv09_You’ll NeverWalk Alone Modeling Social Behavior for Multi-target Tracking 社会行为建模-iccv09_You' ll NeverWalk Alone Modeling Social Behavior for Multi-target Tracking modeling of social behavior
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Size: 2667520 |
Author: 小王 |
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Description: 蚁群优化算法的基本思想是模仿蚂蚁依赖信息素进行通信而显示出的社会行为-The basic idea of ant colony optimization algorithm is to mimic the ants rely on pheromones to communicate and show social behavior
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Author: 周同同 |
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Description: A PAPER SHOWING ADVANTAGES OF SOCIAL BEHAVIOR BEING TAKEN INTO ACCOUNT IN OLSR
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Author: hco |
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Description: 这是关于粒子群算法的一篇综述论文，说明了PSO算法的原理，及其算法流程！-PSO is a population-based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling. All of particles have fitness values which are evaluated by the fitness function to be optimized, and have velocities which direct the flying of the particles.
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Size: 319488 |
Author: wanghong |
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Description: Cockroach Swarm Optimization
a new bionic algorithm, entitled
Cockroach Swarm Optimization (CSO), that is inspired by the
social behavior of cockroaches. We construct some models by
imitating the foraging behaviors of cockroach, and describe the
steps of CSO. Simulation results illustrate that CSO has
stronger convergence performance and highly-accuracy
optimization results compares with Particle Swarm
Optimization (PSO), Chaotic Particle Swarm Optimization
(CPSO) and Artificial Fish-swarm Algorithm (AFSA).
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Size: 2048 |
Author: sina valizade |
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Description: 求解非线性方程组方法有经典算法以及近年来流行的遗传算法．牛顿法及其改进形式,但是此类算法的收敛性在很大程度上依赖于初始点的选择，对于某些非线性方程组容易导致求解失败 为了克服经典算法的缺点，设计了求解非线性方程组的混合遗传算法,但依然对方程组和编码方法有很高要求。PSO是受到鸟群或者鱼群社会行为的启发而形成的一种基于种群的随机优化技术。它是一类随机全局优化技术，通过粒子间的相互作用发现复杂搜索空间中的最优区域。该算法是一种基于群体智能的新型演化计算技术，具有简单易实现、设置参数少、全局优化能力强等优点。粒子群优化算法已在函数优化、神经网络设计、分类、模式识别、信号处理、机器人技术等许多领域取得了成功的应用。 本函数包带函数，测试函数与使用简介-Methods for solving nonlinear equations and the recent popular classical algorithm genetic algorithm. Newton s method and its improved form, but such convergence of the algorithm is largely dependent on the choice of the initial point, for some nonlinear equations solved easily lead to failure In order to overcome the shortcomings of classical algorithms designed for solving nonlinear Equations hybrid genetic algorithm, but still on the equations and the encoding method has high requirements. PSO is social behavior by birds or fish inspired the formation of a population-based stochastic optimization techniques. It is a class of stochastic global optimization technique, through complex interaction between particles found in the search space optimum area. The algorithm is a novel based on swarm intelligence evolutionary computation techniques, with a simple and easy to achieve, set few parameters, global optimization ability, etc.. Particle swarm optimization in function optimizatio
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Description: The learning system of collective behavior in students social
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Size: 596992 |
Author: Nitromira |
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Description: 一种遗传算法和萤火虫算法相结合的新型元启发式算法，经过测试，算法具有良好的性能。-Capacitated facility location problem (CFLP) is a well-known combinatorial optimization
problem with applications in distribution and production planning that is classified as an
NP-Hard problem. The aim is to determine where to locate facilities and how to move
commodities such that the customers’ demands are satisfied and the total cost
minimized. In this paper, a new hybrid optimization method called Hybrid Evolutionary
Firefly-Genetic Algorithm is proposed, which is inspired by social behavior of fireflies and
the phenomenon of bioluminescent communication. The method combines the discrete
Firefly Algorithm (FA) with the standard Genetic Algorithm (GA). It is devoted to the
detailed description of the problem, and an adaption of the algorithm. Computational
results on random generated problems consisting of 2000 locations and 2000 customers
are reported.
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Author: 孙雪 |
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Description: 通过利用社会力学，对已有的人群异常行为检测方法进行优化-Abnormal Crowd Behavior Detection by Social Force Optimization
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Author: wangskyqun |
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Description: 多目标粒子群算法是模拟动物群体的社会行为，找到一个最优设计点的过程比作这些生物的觅食活动。换句话说，这些例子在设计空间中寻找最好的位置。-Multi-objective Particle Swarm social behavior is simulated animal groups, a process to find the optimal design point likened foraging activity of these organisms. In other words, these examples to find the best location in the design space.
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Author: 王军 |
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Description: 受帝国主义殖民竞争机制的启发，Atashpaz-Gargari和Lucas于2007年提出了一种新的智能优化算法—帝国竞争算法 (ICA)。与GA, PSO, ABC等受生物行为启发的群智能算法不同，ICA受社会行为启发，通过摸拟殖民地同化机制和帝国竞争机制而形成的一种优化方法。ICA也是一种基于群体的优化方法，其解空间由称为国家的个体组成。ICA将国家分为几个子群，称为帝国。在每个帝国内，ICA通过同化机制使非最优的国家(殖民地)向最优国家(帝国主义国家)靠近，该过程类似于PSO。帝国竞争机制是ICA的关键，ICA通过帝国竞争机制将最弱帝国中的一个或多个殖民地移动到其他帝国，使帝国之间可以进行信息交互。(Inspired by the imperialist colonial competition mechanism, Atashpaz-Gargari and Lucas proposed a new intelligent optimization algorithm, Empire competition algorithm (ICA), in 2007. With GA, PSO, ABC and other biological behavior of swarm intelligence algorithm inspired by social behavior, ICA heuristic, an optimization method is formed by simulation of colonial assimilation mechanism and competition mechanism of the empire. ICA is also a swarm based optimization approach whose solution space consists of individuals called states. ICA divides the country into several subgroups, called empires. Within each Empire, ICA moves the non optimal country (colony) to the best country (the imperialist state) through the assimilation mechanism, which is similar to PSO. Imperial competition is the key to ICA, and ICA moves one or more colonies of the weakest Empire to other empires through imperial competition, allowing the Empire to interact with each other.)
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Size: 16384 |
Author: xzfff
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Description: 推荐Netflix，亚马逊系统Spotify，例如，可以推荐我们基于我们的历史的新东西，行为和我们玩什么内容。但是当一个新用户到达没有数据的系统时会发生什么呢？我们向他推荐什么？这是冷启动问题。(Recommending systems like Netflix, Spotify, amazon, for instance, can recommend us new stuff based on our history, behavior and what content we play. But what happens when a new user arrives in the system with no data? What to we recommend to him? That's the cold start issue.)
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Author: jun8814
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Description: 人工蜂群算法自2005年被Karaboga等人提出以来，以其操作简单、参数少、易于编程实现、收敛速度快等特点而受到越来越多的关注。2007年，Karaboga【2007】使用人工蜂群算法对多变量函数进行优化，并对由人工蜂群算法（ABC），遗传算法（GA），粒子温度算法（PSO）和粒子温度灵敏演化算法（PS-EA）产生的结果进行了比较。 结果表明，人工蜂群算法优于其他算法。2009年，Karaboga【2009】使用人工蜂群算法优化大量的数值函数，并对由人工蜂群算法（ABC），遗传算法（GA），粒子温度算法（PSO），差分演化算法（DS）和进化策略（ES）产生的结果进行了比较。 结果表明，人工蜂群算法因其控制参数少、操作简群算单优于或类似于其他算法。(Artificial Bee Colony (ABC) algorithm (Dervis Karaboga 2005 [1]; Karaboga and Basturk 2009[2]), simulating the intelligent social behavior of a group of bees, aims to solve the numerical optimization problem in a given condition. Many scientific theories and engineering applications in real life can be attributed to the numerical optimization problem. For applications where there is no optimal solution or approximate solution, ABC optimization algorithm can show its advantages in a short period of time and give it a term that can be terminated at any time. Initializing a set of random solution at the very beginning and searching the optimization value with iteration according to candidate solutions generated by a certain strategy, ABC algorithm solves the problems effectively and efficiently. Due to these advantages, ABC optimization algorithm has been increasingly popular since it has been proposed by Dervis Karaboga in 2005 [1].)
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Author: Becky7163 |
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Description: 蚁群优化是一组概率性元启发式算法和智能优化算法，受蚂蚁社会行为的启发。(Ant Colony Optimization (ACO) are a set of probabilistic metaheuristics and an intelligent optimization algorithms, inspired by social behavior of ants.)
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Author: 不喜欢写论文 |
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Description: 粒子群优化算起源于对鸟群、鱼群以及对某些社会行为的模拟，是一种基于群体智能的进化计算技术。而小生境技术则起源于遗传算法，这种方法能使基于群体的随机优化算法形成物种，从而使相应的优化算法具有发现多个最优解的能力。而多分类器集成技术则是通过多个分类器进行某种组合来决定最终的分类，以取得比单个分类器更好的性能。多分类器集成技术要求基元分类器不仅个体性能要好并且其差异度要大，这与小生境技术形成物种的能力具有很多内在的相似性。目前己经有研究者将小生境技术应用于多分类器集成，但由于传统的小生境技术仍然不完善，存在一些内在的陷，因而这些应用还不成熟和完善。
(Particle swarm optimization (partieleSwarmOptimization) originated in the birds, fish, and of a
Some simulation of social behavior, is a swarm intelligence-based evolutionary computing. The origin of the niche technology is In genetic algorithms, this method can make random optimization algorithm based on the formation of groups of species, so that the appropriate priority
Algorithm has the ability to find multiple optimal solutions. The integration technology of multiple classifiers is through multiple classifiers into
Some combination of the line to determine the final classification, in order to obtain better than a single classifier performance. Integration of multiple classifiers
Technical requirements for primitive classification is not only better individual performance and the difference to a large degree, which form a niche technology The ability of species has many inherent similarities. The researchers will now have a niche technology used in multisection
Class ens)
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Author: dreamer |
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